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+++ b/docs/_sources/changelog.rst.txt @@ -5,6 +5,14 @@ matminer Changelog ================== +**v0.4.8** + +* CGCNN featurizer / model (Q. Wang, T. Xie) +* Text-mined element embedding featurizer (A. Dunn) +* add Brgoch data set (D. Dopp) +* add quartile to PropertyStats (A. Dunn) +* minor fixes, improvements (A. Dunn, A. Jain) + **v0.4.6** * Jarvis CFID descriptors (A. Dunn, K. Choudhary) diff --git a/docs/_sources/contributors.rst.txt b/docs/_sources/contributors.rst.txt index dedb562eb..0c153ce08 100644 --- a/docs/_sources/contributors.rst.txt +++ b/docs/_sources/contributors.rst.txt @@ -52,4 +52,5 @@ Other * David Waroquiers (UC Louvain, Belgium) * Logan Williams (U Michigan Ann Arbor) * Aidan Thompson (Sandia National Lab) -* Kamal Choudhary (NIST) \ No newline at end of file +* Kamal Choudhary (NIST) +* Tian Xie (MIT) \ No newline at end of file diff --git a/docs/_sources/dataset_summary.rst.txt b/docs/_sources/dataset_summary.rst.txt index aaef57849..b32bb4fad 100644 --- a/docs/_sources/dataset_summary.rst.txt +++ b/docs/_sources/dataset_summary.rst.txt @@ -70,6 +70,60 @@ publisher = {Dryad Digital Repository} +------------------------- +brgoch_superhard_training +------------------------- +2574 materials used for training regressors that predict shear and bulk modulus. + +**Number of entries:** 2574 + +============= ============================================================================================================================================================ +Column Description +============= ============================================================================================================================================================ +brgoch_feats features used in brgoch study compressed to a dictionary +bulk_modulus VRH bulk modulus +composition pymatgen composition object +formula Chemical formula as a string +material_id materials project id +structure pymatgen structure object +shear_modulus VRH shear modulus +suspect_value True if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found +============= ============================================================================================================================================================ + + + +**Reference** + +Machine Learning Directed Search for Ultraincompressible, Superhard Materials +Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch +Journal of the American Chemical Society 2018 140 (31), 9844-9853 +DOI: 10.1021/jacs.8b02717 + + + +**Bibtex Formatted Citations** + +@article{doi:10.1021/jacs.8b02717, +author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah}, +title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials}, +journal = {Journal of the American Chemical Society}, +volume = {140}, +number = {31}, +pages = {9844-9853}, +year = {2018}, +doi = {10.1021/jacs.8b02717}, +note ={PMID: 30010335}, +URL = { +https://doi.org/10.1021/jacs.8b02717 +}, +eprint = { +https://doi.org/10.1021/jacs.8b02717 +} +} + + + + -------------------- castelli_perovskites -------------------- @@ -622,6 +676,48 @@ https://doi.org/10.1021/acs.jpclett.7b01046 +--------------- +glass_binary_v2 +--------------- +Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1. + +**Number of entries:** 5483 + +======= ================================================================================================================================================================================= +Column Description +======= ================================================================================================================================================================================= +formula chemical formula +gfa glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming ("AM"), 0: non-full-forming("CR") +======= ================================================================================================================================================================================= + + + +**Reference** + +https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046 + + + +**Bibtex Formatted Citations** + +@article{doi:10.1021/acs.jpclett.7b01046, +author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.}, +title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability}, +journal = {The Journal of Physical Chemistry Letters}, +volume = {8}, +number = {14}, +pages = {3434-3439}, +year = {2017}, +doi = {10.1021/acs.jpclett.7b01046}, +note ={PMID: 28697303}, +eprint = { +https://doi.org/10.1021/acs.jpclett.7b01046 + +}} + + + + ------------------ glass_ternary_hipt ------------------ diff --git a/docs/_sources/featurizer_summary.rst.txt b/docs/_sources/featurizer_summary.rst.txt index d07002855..83d6ebe49 100644 --- a/docs/_sources/featurizer_summary.rst.txt +++ b/docs/_sources/featurizer_summary.rst.txt @@ -171,6 +171,7 @@ Name Description :code:`ChemicalOrdering` How much the ordering of species in the structure differs from random `[more] `_ :code:`StructureComposition` Features related to the composition of a structure `[more] `_ :code:`XRDPowderPattern` 1D array representing powder diffraction of a structure as calculated by `[more] `_ +:code:`CGCNNFeaturizer` Features generated by training a Crystal Graph Convolutional Neural Network `[more] `_ :code:`JarvisCFID` Classical Force-Field Inspired Descriptors (CFID) from Jarvis-ML. `[more] `_ ============================================ ================================================================================================================================================================================================================================================================================================================================================================================================================ diff --git a/docs/_sources/matminer.featurizers.utils.rst.txt b/docs/_sources/matminer.featurizers.utils.rst.txt index e78806815..7209f3104 100644 --- a/docs/_sources/matminer.featurizers.utils.rst.txt +++ b/docs/_sources/matminer.featurizers.utils.rst.txt @@ -4,6 +4,14 @@ matminer.featurizers.utils package Submodules ---------- +matminer.featurizers.utils.cgcnn module +--------------------------------------- + +.. automodule:: matminer.featurizers.utils.cgcnn + :members: + :undoc-members: + :show-inheritance: + matminer.featurizers.utils.grdf module -------------------------------------- diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 19ced1057..104f076ae 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -81,6 +81,10 @@ div.sphinxsidebar input { font-size: 1em; } +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + div.sphinxsidebar #searchbox input[type="text"] { float: left; width: 80%; @@ -427,6 +431,13 @@ table.field-list td, table.field-list th { hyphens: manual; } +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist td { + vertical-align: top; +} + + /* -- other body styles ----------------------------------------------------- */ ol.arabic { diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js index 0c15c0099..ffadbec11 100644 --- a/docs/_static/doctools.js +++ b/docs/_static/doctools.js @@ -70,7 +70,9 @@ jQuery.fn.highlightText = function(text, className) { if (node.nodeType === 3) { var val = node.nodeValue; var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) { + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { var span; var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); if (isInSVG) { @@ -148,7 +150,9 @@ var Documentation = { this.fixFirefoxAnchorBug(); this.highlightSearchWords(); this.initIndexTable(); - + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } }, /** @@ -308,4 +312,4 @@ _ = Documentation.gettext; $(document).ready(function() { Documentation.init(); -}); \ No newline at end of file +}); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 24c291d0d..c163a9343 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,9 +1,296 @@ var DOCUMENTATION_OPTIONS = { - URL_ROOT: '', - VERSION: '0.4.6', + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '0.4.8', LANGUAGE: 'None', COLLAPSE_INDEX: false, FILE_SUFFIX: '.html', HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt' -}; \ No newline at end of file + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SEARCH_LANGUAGE_STOP_WORDS: ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"] +}; + + + +/* Non-minified version JS is _stemmer.js if file is provided */ +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + + + + + +var splitChars = (function() { + var result = {}; + var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, + 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, + 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, + 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, + 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, + 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, + 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, + 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, + 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, + 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; + var i, j, start, end; + for (i = 0; i < singles.length; i++) { + result[singles[i]] = true; + } + var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], + [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], + [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], + [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], + [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], + [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], + [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], + [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], + [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], + [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], + [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], + [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], + [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], + [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], + [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], + [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], + [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], + [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], + [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], + [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], + [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], + [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], + [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], + [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], + [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], + [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], + [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], + [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], + [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], + [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], + [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], + [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], + [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], + [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], + [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], + [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], + [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], + [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], + [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], + [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], + [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], + [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], + [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], + [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], + [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], + [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], + [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], + [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], + [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; + for (i = 0; i < ranges.length; i++) { + start = ranges[i][0]; + end = ranges[i][1]; + for (j = start; j <= end; j++) { + result[j] = true; + } + } + return result; +})(); + +function splitQuery(query) { + var result = []; + var start = -1; + for (var i = 0; i < query.length; i++) { + if (splitChars[query.charCodeAt(i)]) { + if (start !== -1) { + result.push(query.slice(start, i)); + start = -1; + } + } else if (start === -1) { + start = i; + } + } + if (start !== -1) { + result.push(query.slice(start)); + } + return result; +} + + diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js index 41b833677..7473859b2 100644 --- a/docs/_static/searchtools.js +++ b/docs/_static/searchtools.js @@ -1,5 +1,5 @@ /* - * searchtools.js_t + * searchtools.js * ~~~~~~~~~~~~~~~~ * * Sphinx JavaScript utilities for the full-text search. @@ -9,323 +9,44 @@ * */ - -/* Non-minified version JS is _stemmer.js if file is provided */ -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' +if (!Scorer) { + /** + * Simple result scoring code. + */ + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [filename, title, anchor, descr, score] + // and returns the new score. + /* + score: function(result) { + return result[4]; + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: {0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5}, // used to be unimportantResults + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + // query found in terms + term: 5 }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } } - - -/** - * Simple result scoring code. - */ -var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [filename, title, anchor, descr, score] - // and returns the new score. - /* - score: function(result) { - return result[4]; - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: {0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5}, // used to be unimportantResults - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - // query found in terms - term: 5 -}; - - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if (start !== -1) { - result.push(query.slice(start)); - } - return result; +if (!splitQuery) { + function splitQuery(query) { + return query.split(/\s+/); + } } - - - /** * Search Module */ @@ -417,7 +138,7 @@ var Search = { */ query : function(query) { var i; - var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"]; + var stopwords = DOCUMENTATION_OPTIONS.SEARCH_LANGUAGE_STOP_WORDS; // stem the searchterms and add them to the correct list var stemmer = new Stemmer(); @@ -758,4 +479,4 @@ var Search = { $(document).ready(function() { Search.init(); -}); \ No newline at end of file +}); diff --git a/docs/changelog.html b/docs/changelog.html index fa3b133ce..cda8dc164 100644 --- a/docs/changelog.html +++ b/docs/changelog.html @@ -6,10 +6,10 @@ - MatMiner Changelog — matminer 0.4.6 documentation + MatMiner Changelog — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -39,6 +39,14 @@

    Navigation

    matminer ChangelogΒΆ

    +

    v0.4.8

    +
      +
    • CGCNN featurizer / model (Q. Wang, T. Xie)
      • +
    • Text-mined element embedding featurizer (A. Dunn)
      • +
    • add Brgoch data set (D. Dopp)
      • +
    • add quartile to PropertyStats (A. Dunn)
      • +
    • minor fixes, improvements (A. Dunn, A. Jain)
      • +

    v0.4.6

    diff --git a/docs/contributors.html b/docs/contributors.html index b538ff3c6..966087fde 100644 --- a/docs/contributors.html +++ b/docs/contributors.html @@ -6,10 +6,10 @@ - MatMiner Contributors — matminer 0.4.6 documentation + MatMiner Contributors — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -85,6 +85,7 @@

    Other
  • Logan Williams (U Michigan Ann Arbor)
  • Aidan Thompson (Sandia National Lab)
  • Kamal Choudhary (NIST)
    • +
  • Tian Xie (MIT)
    • @@ -95,7 +96,7 @@

    Other @@ -285,7 +285,7 @@

    6. Make a Pull Request to the matminer GitHub repository
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/dataset_summary.html b/docs/dataset_summary.html index 596734fa2..d0a388592 100644 --- a/docs/dataset_summary.html +++ b/docs/dataset_summary.html @@ -6,10 +6,10 @@ - Datasets Available in Matminer — matminer 0.4.6 documentation + Datasets Available in Matminer — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -117,6 +117,71 @@

    boltztrap_mp +

    brgoch_superhard_trainingΒΆ

    +

    2574 materials used for training regressors that predict shear and bulk modulus.

    +

    Number of entries: 2574

    + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ColumnDescription
    brgoch_featsfeatures used in brgoch study compressed to a dictionary
    bulk_modulusVRH bulk modulus
    compositionpymatgen composition object
    formulaChemical formula as a string
    material_idmaterials project id
    structurepymatgen structure object
    shear_modulusVRH shear modulus
    suspect_valueTrue if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found
    +

    Reference

    +

    Machine Learning Directed Search for Ultraincompressible, Superhard Materials +Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch +Journal of the American Chemical Society 2018 140 (31), 9844-9853 +DOI: 10.1021/jacs.8b02717

    +

    Bibtex Formatted Citations

    +

    @article{doi:10.1021/jacs.8b02717, +author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah}, +title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials}, +journal = {Journal of the American Chemical Society}, +volume = {140}, +number = {31}, +pages = {9844-9853}, +year = {2018}, +doi = {10.1021/jacs.8b02717}, +note ={PMID: 30010335}, +URL = { +https://doi.org/10.1021/jacs.8b02717 +}, +eprint = { +https://doi.org/10.1021/jacs.8b02717 +} +}

    +

    castelli_perovskitesΒΆ

    18,928 perovskites generated with ABX combinatorics, calculating gllbsc band gap and pbe structure, and also reporting absolute band edge positions and heat of formation.

    @@ -769,6 +834,46 @@

    glass_binary +

    glass_binary_v2ΒΆ

    +

    Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1.

    +

    Number of entries: 5483

    + ++++ + + + + + + + + + + + + + +
    ColumnDescription
    formulachemical formula
    gfaglass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming (β€œAM”), 0: non-full-forming(β€œCR”)
    +

    Reference

    +

    https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046

    +

    Bibtex Formatted Citations

    +

    @article{doi:10.1021/acs.jpclett.7b01046, +author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.}, +title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability}, +journal = {The Journal of Physical Chemistry Letters}, +volume = {8}, +number = {14}, +pages = {3434-3439}, +year = {2017}, +doi = {10.1021/acs.jpclett.7b01046}, +note ={PMID: 28697303}, +eprint = { +https://doi.org/10.1021/acs.jpclett.7b01046

    +

    }}

    +

    glass_ternary_hiptΒΆ

    Metallic glass formation dataset for ternary alloys, collected from the high-throughput sputtering experiments measuring whether it is possible to form a glass using sputtering. The hipt experimental data are of the Co-Fe-Zr, Co-Ti-Zr, Co-V-Zr and Fe-Ti-Nb ternary systems.

    @@ -1737,10 +1842,11 @@

    wolverton_oxides
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/example_bulkmod.html b/docs/example_bulkmod.html index 16a1b5190..9b0bc23dc 100644 --- a/docs/example_bulkmod.html +++ b/docs/example_bulkmod.html @@ -6,10 +6,10 @@ - Predicting bulk moduli with matminer — matminer 0.4.6 documentation + Predicting bulk moduli with matminer — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -312,7 +312,7 @@

    Step 6: Plot our results and determine what features are the most important< @@ -353,7 +353,10 @@

    Generating features based on a material’s crystal structure.XRDPowderPattern 1D array representing powder diffraction of a structure as calculated by [more] -JarvisCFID +CGCNNFeaturizer +Features generated by training a Crystal Graph Convolutional Neural Network [more] + +JarvisCFID Classical Force-Field Inspired Descriptors (CFID) from Jarvis-ML. [more] @@ -368,7 +371,7 @@

    Generating features based on a material’s crystal structure.
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/genindex.html b/docs/genindex.html index fe2c93ae7..87d88309b 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -7,10 +7,10 @@ - Index — matminer 0.4.6 documentation + Index — matminer 0.4.8 documentation - + @@ -29,7 +29,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -59,6 +59,7 @@

    Index

    | N | O | P + | Q | R | S | T @@ -174,6 +175,8 @@

    _

  • (matminer.featurizers.structure.BagofBonds method)
  • (matminer.featurizers.structure.BondFractions method) +
    • +
  • (matminer.featurizers.structure.CGCNNFeaturizer method)
  • (matminer.featurizers.structure.ChemicalOrdering method)
    • @@ -208,6 +211,12 @@

    _

  • (matminer.featurizers.structure.StructureComposition method)
  • (matminer.featurizers.structure.XRDPowderPattern method) +
    • +
  • (matminer.featurizers.utils.cgcnn.AtomCustomArrayInitializer method) +
    • +
  • (matminer.featurizers.utils.cgcnn.CIFDataWrapper method) +
    • +
  • (matminer.featurizers.utils.cgcnn.CrystalGraphConvNetWrapper method)
  • (matminer.featurizers.utils.grdf.Bessel method)
    • @@ -226,6 +235,8 @@

    _

  • (matminer.utils.data.DemlData method)
  • (matminer.utils.data.MagpieData method) +
    • +
  • (matminer.utils.data.MatscholarElementData method)
  • (matminer.utils.data.MixingEnthalpy method)
    • @@ -264,6 +275,10 @@

    A

    - + @@ -658,6 +683,8 @@

    F

  • (matminer.featurizers.structure.BagofBonds method)
  • (matminer.featurizers.structure.BondFractions method) +
    • +
  • (matminer.featurizers.structure.CGCNNFeaturizer method)
  • (matminer.featurizers.structure.ChemicalOrdering method)
    • @@ -800,6 +827,8 @@

    F

  • (matminer.featurizers.structure.BagofBonds method)
  • (matminer.featurizers.structure.BondFractions method) +
    • +
  • (matminer.featurizers.structure.CGCNNFeaturizer method)
  • (matminer.featurizers.structure.ChemicalOrdering method)
    • @@ -874,6 +903,8 @@

    F

  • (matminer.featurizers.structure.BagofBonds method)
  • (matminer.featurizers.structure.BondFractions method) +
    • +
  • (matminer.featurizers.structure.CGCNNFeaturizer method)
  • (matminer.featurizers.structure.PartialRadialDistributionFunction method)
    • @@ -1018,6 +1049,8 @@

    G

  • (matminer.utils.data.DemlData method)
  • (matminer.utils.data.MagpieData method) +
    • +
  • (matminer.utils.data.MatscholarElementData method)
  • (matminer.utils.data.PymatgenData method)
    • @@ -1184,6 +1217,8 @@

    I

  • (matminer.featurizers.structure.BagofBonds method)
  • (matminer.featurizers.structure.BondFractions method) +
    • +
  • (matminer.featurizers.structure.CGCNNFeaturizer method)
  • (matminer.featurizers.structure.ChemicalOrdering method)
    • @@ -1263,8 +1298,12 @@

    L

    - +
    +

    Q

    + + +
    +

    R

    + + +
    diff --git a/docs/index.html b/docs/index.html index 099329b33..de488d85d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -6,10 +6,10 @@ - matminer (Materials Data Mining) — matminer 0.4.6 documentation + matminer (Materials Data Mining) — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -191,7 +191,7 @@

    Code documentation
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/installation.html b/docs/installation.html index 463c47248..8ee2b66a8 100644 --- a/docs/installation.html +++ b/docs/installation.html @@ -6,10 +6,10 @@ - Installing matminer — matminer 0.4.6 documentation + Installing matminer — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -82,7 +82,7 @@

    TipsΒΆ @@ -551,7 +551,7 @@

    matminer.data_retrieval.retrieve_MPDS module
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.datasets.html b/docs/matminer.datasets.html index 39be3279b..a779a2eec 100644 --- a/docs/matminer.datasets.html +++ b/docs/matminer.datasets.html @@ -6,10 +6,10 @@ - matminer.datasets package — matminer 0.4.6 documentation + matminer.datasets package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -50,7 +50,35 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    +
    +
    download_if_missing (bool): Whether or not to download the dataset if
    +
    it isn’t on disk
    +
    +
    + +

    Returns: (pd.DataFrame)

    + + +
    +
    +matminer.datasets.convenience_loaders.load_brgoch_superhard_training(subset='all', drop_suspect=False, data_home=None, download_if_missing=True)ΒΆ
    +

    Convenience function for loading the expt_formation_enthalpy dataset.

    +
    +
    Args:
    +
    +
    subset (str): Identifier for subset of data to return,
    +
    +
    all: all possible columns including metadata, engineered features,
    +
    and basic descriptors
    +
    +

    brgoch_features: only features from reference paper and targets +basic_descriptors: only composition/structure columns and targets

    +
    +
    drop_suspect (bool): Whether to drop values with possibly incorrect
    +
    elastic data and materials that could not be verified
    +
    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -66,7 +94,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -86,7 +114,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -106,7 +134,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -126,7 +154,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -142,7 +170,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -164,7 +192,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -180,7 +208,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -196,7 +224,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -212,7 +240,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -224,11 +252,15 @@

    Submodules
    -matminer.datasets.convenience_loaders.load_glass_binary(data_home=None, download_if_missing=True)ΒΆ
    +matminer.datasets.convenience_loaders.load_glass_binary(version='v2', data_home=None, download_if_missing=True)ΒΆ

    Convenience function for loading the glass_binary dataset.

    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +
    +
    version (str): Version identifier for dataset, see dataset description
    +
    for explanation of each. Defaults to v2
    +
    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -248,7 +280,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -270,7 +302,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -286,7 +318,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -304,7 +336,7 @@

    Submodulesdrop_nan_columns (list, str): Column or columns to drop rows containing NaN values from

    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -322,7 +354,7 @@

    Submodulesdrop_nan_columns (list, str): Column or columns to drop rows containing NaN values from

    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -340,7 +372,7 @@

    Submodulesdrop_nan_columns (list, str): Column or columns to drop rows containing NaN values from

    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -356,7 +388,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -376,7 +408,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -392,7 +424,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -412,7 +444,7 @@

    Submodules
    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -428,7 +460,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -444,7 +476,7 @@

    Submodules
    Args:
    -

    data_home (str, None): Where to loom for and store the loaded dataset

    +

    data_home (str, None): Where to look for and store the loaded dataset

    download_if_missing (bool): Whether or not to download the dataset if
    it isn’t on disk
    @@ -598,7 +630,7 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.featurizers.html b/docs/matminer.featurizers.html index c9210d60e..fb0fa1c5f 100644 --- a/docs/matminer.featurizers.html +++ b/docs/matminer.featurizers.html @@ -6,10 +6,10 @@ - matminer.featurizers package — matminer 0.4.6 documentation + matminer.featurizers package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -45,6 +45,7 @@

    Subpackagesmatminer.featurizers.utils package

    +

    Returns:

    @@ -1578,7 +1584,11 @@

    Submodules +
    preset_name: (str) can be one of β€œmagpie”, β€œdeml”, β€œmatminer”, or
    +
    β€œmatscholar_el”.
    +

    +

    Returns:

    @@ -2225,8 +2235,8 @@

    Submodules and r_i are the fraction and radius of -element i, and
    +

    where c_i and r_i are the fraction and radius of +element i, and

    System Message: WARNING/2 (ar{r})

    latex exited with error [stdout] @@ -2252,6 +2262,7 @@

    Submodules`

    rac{T_m Delta S_{mix}}{ | Delta H_{mix} | }`

    -

    Where T_m is average melting temperature, -\Delta S_{mix} is the ideal mixing entropy, -and \Delta H_{mix} is the average mixing enthalpies +

    Where T_m is average melting temperature, +\Delta S_{mix} is the ideal mixing entropy, +and \Delta H_{mix} is the average mixing enthalpies of all pairs of elements in the alloy

    Args:
    @@ -3108,7 +3119,7 @@

    Submodules
    -static from_preset(cation_anion=False)ΒΆ
    +static from_preset(preset, cation_anion=False)ΒΆ

    Use preset parameters to get the fingerprint Args:

    @@ -3649,11 +3660,11 @@

    Submodules is the index of the atom, j is the index of a neighboring atom, \eta is a scaling function, -r_{ij} is the distance between atoms i and j, and f(r) is a cutoff function where +

    -
    rac{pi r_{ij}}{R_c}) + 1]` if r < R_c:math: and 0 otherwise.
    +
    rac{pi r_{ij}}{R_c}) + 1]` if r < R_c:math: and 0 otherwise.
    The direction-resolved fingerprints are computed using :math:`V_i^k(eta) = sumlimits_{i
    @@ -3661,7 +3672,7 @@

    Submodules is the k^{th} component of
    +
    where r_{ij}^k is the k^{th} component of

    System Message: WARNING/2 (old{r}_i - old{r}_j)

    latex exited with error [stdout] @@ -3687,6 +3698,7 @@

    Submodules
    -static from_preset(width=0.5, spacing=0.5, cutoff=10)ΒΆ
    +static from_preset(preset, width=0.5, spacing=0.5, cutoff=10)ΒΆ
    Preset bin functions for this featurizer. Example use:
    >>> AFS = AngularFourierSeries.from_preset('gaussian')
@@ -4073,13 +4085,13 @@ 
    Submodules and
-\hat{W} parameters proposed by Steinhardt et al..
    
+
    In addition to the average spherical harmonics, this class can also compute the W and
+\hat{W} parameters proposed by Steinhardt et al..
    
 
    
 
    Attributes:
    
 
    BOOP Q l=<n> - Average spherical harmonic for a certain degree, n.
 BOOP W l=<n> - W parameter for a certain degree of spherical harmonic, n.
-BOOP What l=<n> - \hat{W} parameter for a certain degree of spherical harmonic, n.
    
+BOOP What l=<n> - \hat{W} parameter for a certain degree of spherical harmonic, n.
    References:
    Steinhardt et al., _PRB_ (1983) Seko et al., _PRB_ (2017)
    @@ -4226,7 +4238,7 @@

    Submodules
    -static from_preset()ΒΆ
    +static from_preset(preset)ΒΆ

    Use a standard collection of CE types and choose your ChemEnv neighbor-finding strategy. Args:

    @@ -4350,9 +4362,9 @@

    Submodulesself.el_list_ will be featurized. +in the self.el_list_ will be featurized. Besides, compositions of the passed structures will also be β€œstored” -in a dict of self.el_amt_dict_, avoiding repeated calculation of +in a dict of self.el_amt_dict_, avoiding repeated calculation of composition when featurizing multiple sites in the same structure. Args:

    @@ -4382,12 +4394,12 @@

    Submodules
    -static from_preset(**kwargs)ΒΆ
    +static from_preset(preset, **kwargs)ΒΆ

    Use one of the standard instances of a given NearNeighbor class. Args:

    -
    preset (str): preset type (β€œVoronoiNN”, β€œJMolNN”,
    +
    preset (str): preset type (β€œVoronoiNN”, β€œJmolNN”,
    β€œMiniumDistanceNN”, β€œMinimumOKeeffeNN”, or β€œMinimumVIRENN”).
    @@ -4501,12 +4513,12 @@

    Submodules
    -static from_preset(**kwargs)ΒΆ
    +static from_preset(preset, **kwargs)ΒΆ

    Use one of the standard instances of a given NearNeighbor class. Args:

    -
    preset (str): preset type (β€œVoronoiNN”, β€œJMolNN”,
    +
    preset (str): preset type (β€œVoronoiNN”, β€œJmolNN”,
    β€œMiniumDistanceNN”, β€œMinimumOKeeffeNN”, or β€œMinimumVIRENN”).
    @@ -4607,7 +4619,7 @@

    Submodules
    -static from_preset(**kwargs)ΒΆ
    +static from_preset(preset, **kwargs)ΒΆ

    Use preset parameters to get the fingerprint Args:

    @@ -4770,7 +4782,7 @@

    Submodules
    -static cosine_cutoff(cutoff)ΒΆ
    +static cosine_cutoff(rs, cutoff)ΒΆ

    Polynomial cutoff function to give a smoothed truncation of the Gaussian symmetry functions. Args:

    @@ -4810,7 +4822,7 @@

    Submodules
    -static g2(rs, cutoff)ΒΆ
    +static g2(eta, rs, cutoff)ΒΆ

    Gaussian radial symmetry function of the center atom, given an eta parameter. Args:

    @@ -4826,7 +4838,7 @@

    Submodules
    -static g4(zetas, gammas, neigh_dist, neigh_coords, cutoff)ΒΆ
    +static g4(etas, zetas, gammas, neigh_dist, neigh_coords, cutoff)ΒΆ

    Gaussian angular symmetry function of the center atom, given a set of eta, zeta and gamma parameters. Args:

    @@ -4979,7 +4991,7 @@

    Submodules
    -static from_preset(width=1.0, spacing=1.0, cutoff=10, mode='GRDF')ΒΆ
    +static from_preset(preset, width=1.0, spacing=1.0, cutoff=10, mode='GRDF')ΒΆ
    Preset bin functions for this featurizer. Example use:
    >>> GRDF = GeneralizedRadialDistributionFunction.from_preset('gaussian')
@@ -5021,16 +5033,16 @@ 
    Submodules) a
-weight (A_n) that corresponds to the area of the facet
+neighbors of the site, and assigns each neighbor (n) a
+weight (A_n) that corresponds to the area of the facet
 on the tessellation corresponding to that neighbor.
 The local property difference is then computed by
 :math:`

    -
    rac{sum_n {A_n |p_n - p_0|}}{sum_n {A_n}}`
    -

    where p_n is the property (e.g., atomic number) of a neighbor -and p_0 is the property of a site. If signed parameter is assigned +

    rac{sum_n {A_n |p_n - p_0|}}{sum_n {A_n}}`
    +

    where p_n is the property (e.g., atomic number) of a neighbor +and p_0 is the property of a site. If signed parameter is assigned True, signed difference of the properties is returned instead of absolute difference.

    @@ -5109,7 +5121,7 @@

    Submodules
    -static from_preset()ΒΆ
    +static from_preset(preset)ΒΆ

    Create a new LocalPropertyDifference class according to a preset

    Args:
    @@ -5305,7 +5317,7 @@

    Submodules
    -static from_preset()ΒΆ
    +static from_preset(preset)ΒΆ

    Create the class with pre-defined settings

    Args:
    @@ -5781,12 +5793,12 @@

    Submodules
    -static from_preset(**kwargs)ΒΆ
    +static from_preset(preset, **kwargs)ΒΆ

    Use one of the standard instances of a given NearNeighbor class. Pass args to __init__, such as allowed_bonds, using this method as well.

    Args:
    -
    preset (str): preset type (β€œCrystalNN”, β€œVoronoiNN”, β€œJMolNN”, +
    preset (str): preset type (β€œCrystalNN”, β€œVoronoiNN”, β€œJmolNN”, β€œMiniumDistanceNN”, β€œMinimumOKeeffeNN”, or β€œMinimumVIRENN”).
    Returns:
    CoordinationNumber from a preset.
    @@ -5811,6 +5823,240 @@

    Submodules +
    +class matminer.featurizers.structure.CGCNNFeaturizer(task='classification', atom_init_fea=None, pretrained_name=None, warm_start_file=None, warm_start_latest=False, save_model_to_dir=None, save_checkpoint_to_dir=None, checkpoint_interval=100, del_checkpoint=True, **cgcnn_kwargs)ΒΆ
    +

    Bases: matminer.featurizers.base.BaseFeaturizer

    +

    Features generated by training a Crystal Graph Convolutional Neural Network +(CGCNN) model.

    +
    +
    This featurizer requires a CGCNN model that can either be:
    +
      +
    1. from a pretrained model, currently only supports the models from +the CGCNN repo (12/10/18): https://github.com/txie-93/cgcnn;
      2. +
    2. train a CGCNN model based on the X (structures) and y (target) from +fresh start;
      3. +
    3. similar to 2), but train a model from a warm_start model that can +either be a pretrained model or saved checkpoints.
      4. +
    +
    +
    +

    Please see the fit function for more details.

    +

    After obtaining a CGCNN model, we will featurize the structures by taking +the crystal feature vector obtained after pooling as the features.

    +

    This featurizer requires installing cgcnn and torch. We wrap and refractor +some of the classes and functions from the original cgcnn to make them +work better for matminer. Please also see utils/cgcnn for more details.

    +
    +
    Features:
    +
      +
    • Features for the structures extracted from CGCNN model after pooling.
      • +
    +
    +
    +
    +
    +__init__(task='classification', atom_init_fea=None, pretrained_name=None, warm_start_file=None, warm_start_latest=False, save_model_to_dir=None, save_checkpoint_to_dir=None, checkpoint_interval=100, del_checkpoint=True, **cgcnn_kwargs)ΒΆ
    +
    +
    Args:
    +
    +
    task (str):
    +
    Task type, β€œclassification” or β€œregression”.
    +
    atom_init_fea (dict):
    +
    A dict of {atom type: atom feature}. If not provided, will use +the default atom features from the CGCNN repo.
    +
    pretrained_name (str):
    +
    CGCNN pretrained model name, if None don’t use pre-trained model
    +
    warm_start_file (str):
    +
    The warm start model file, if None, don’t warm start.
    +
    warm_start_latest(bool):
    +
    Warm start from the latest model or best model. +This is set because we customize our checkpoints to contain both +best model and latest model. And if the warm start model does +not contain these two options, will just use the static_dict +given in the model/checkpoints to warm start.
    +
    save_model_to_dir (str):
    +
    Whether to save the best model to disk, if None, don’t save, +otherwise, save the best model to β€˜save_model_to_dir’ path.
    +
    save_checkpoint_to_dir (str):
    +
    Whether to save checkpoint during training, if None, don’t save, +otherwise, save the it to β€˜save_checkpoint_to_dir’ path.
    +
    checkpoint_interval (int):
    +
    Save checkpoint every n epochs if save_checkpoint_to_dir is not +None. If the epochs is less than this checkpoint_interval, will +reset the checkpoint_interval as int(epochs/2).
    +
    del_checkpoint (bool):
    +
    Whether to delete checkpoints if training ends successfully.
    +
    **cgcnn_kwargs (optional): settings of CGCNN, containing:
    +
    +
    CrystalGraphConvNet model kwargs:
    +
    +
    -atom_fea_len (int): Number of hidden atom features in conv
    +
    layers, default 64.
    +
    +

    -n_conv (int): Number of conv layers, default 3. +-h_fea_len (int): Number of hidden features after pooling,

    +
    +
    default 128.
    +

    -n_epochs (int): Number of total epochs to run, default 30. +-print_freq (bool): Print frequency, default 10. +-test (bool): Whether to save test predictions +-task (str): β€œclassification” or β€œregression”,

    +
    +
    default β€œclassification”.
    +
    +
    Dataset (CIFDataWrapper) kwargs:
    +
    +
    -max_num_nbr (int): The maximum number of neighbors while
    +
    constructing the crystal graph, default 12
    +
    -radius (float): The cutoff radius for searching neighbors,
    +
    default 8
    +
    -dmin (float): The minimum distance for constructing
    +
    GaussianDistance, default 0
    +
    -step (float): The step size for constructing
    +
    GaussianDistance, default 0.2
    +
    -random_seed (int): Random seed for shuffling the dataset,
    +
    default 123
    +
    +
    +
    DataLoader kwargs:
    +

    batch_size (int): Mini-batch size, default 256 +num_workers (int): Number of data loading workers, default 0 +train_size (int): Number of training data to be loaded,

    +
    +
    default none
    +
    +
    val_size (int): Number of validation data to be loaded,
    +
    default 1000
    +
    test_size (int): Number of test data to be loaded,
    +
    default 1000
    +
    β€œreturn_test” (bool): Whether to return the test dataset
    +
    loader. default True
    +
    +
    +
    Optimizer kwargs:
    +
    +
    -optim (str): Choose an optimizer, β€œSGD” or β€œAdam”,
    +
    default β€œSGD”.
    +
    +

    -lr (float): Initial learning rate, default 0.01 +-momentum (float): Momentum, default 0.9 +-weight_decay (float): Weight decay (default: 0)

    +
    +
    Scheduler MultiStepLR kwargs:
    +
    +
    -gamma (float): Multiplicative factor of learning rate
    +
    decay, default: 0.1.
    +
    -lr_milestones (list): List of epoch indices.
    +
    Must be increasing.
    +
    +
    +
    +

    These input cgcnn_kwargs will be processed and grouped in +_initialize_kwargs.

    +
    +
    +
    +
    +
    + +
    +
    +citations()ΒΆ
    +

    Citation(s) and reference(s) for this feature.

    +
    +
    Returns:
    +
    +
    (list) each element should be a string citation,
    +
    ideally in BibTeX format.
    +
    +
    +
    +
    + +
    +
    +feature_labels()ΒΆ
    +

    Generate attribute names.

    +
    +
    Returns:
    +
    ([str]) attribute labels.
    +
    +
    + +
    +
    +featurize(strc)ΒΆ
    +

    Get the feature vector after pooling layer of the CGCNN model obtained +from fit. +Args:

    +
    +
    strc (Structure): Structure object
    +
    +
    Returns:
    +
    Features extracted after the pooling layer in CGCNN model
    +
    +
    + +
    +
    +fit(X, y)ΒΆ
    +

    Get a CGCNN model that can either be: +1) from a pretrained model, currently only supports the models from

    +
    +
    the CGCNN repo;
    +
      +
    1. train a CGCNN model based on the X (structures) and y (target) from +fresh start;
      2. +
    2. similar to 2), but train a model from a warm_start model that can +either be a pretrained model or saved checkpoints.
      3. +
    +

    Note that to use CGCNNFeaturizer, a target y is needed! +Args:

    +
    +
    +
    X (Series/list):
    +
    An iterable of pymatgen Structure objects.
    +
    y (Series/list):
    +
    Target property that CGCNN is designed to predict.
    +
    +
    +
    +
    Returns:
    +
    self
    +
    +
    + +
    +
    +implementors()ΒΆ
    +

    List of implementors of the feature.

    +
    +
    Returns:
    +
    +
    (list) each element should either be a string with author name (e.g.,
    +
    β€œAnubhav Jain”) or a dictionary with required key β€œname” and other +keys like β€œemail” or β€œinstitution” (e.g., {β€œname”: β€œAnubhav +Jain”, β€œemail”: β€œajain@lbl.gov”, β€œinstitution”: β€œLBNL”}).
    +
    +
    +
    +
    + +
    +
    +latest_modelΒΆ
    +

    Get the latest model

    +
    + +
    +
    +modelΒΆ
    +

    Get the best model

    +
    + +

    +
    class matminer.featurizers.structure.ChemicalOrdering(shells=(1, 2, 3), weight='area')ΒΆ
    @@ -5822,16 +6068,16 @@

    Submodules`lpha (t,s) = 1 -

    +

    :math:`lpha (t,s) = 1 -

    rac{sum_n w_n delta (t - t_n)}{x_t sum_n w_n}`

    -

    where w_n is the weight associated with a certain neighbor, -t_p is the type of the neighbor, and x_t is the fraction +

    where w_n is the weight associated with a certain neighbor, +t_p is the type of the neighbor, and x_t is the fraction of type t in the structure. For atoms that are randomly dispersed in a structure, this formula yields 0 for all types. For structures where each site is surrounded only by atoms of another type, this formula -yields large values of alpha.

    +yields large values of alpha.

    The mean absolute value of this parameter across all sites is used as a feature.

    @@ -5919,7 +6165,7 @@

    Submodules|R_i-R_j| +off-diagonal elements M_ij = Z_i*Z_j/|R_i-R_j| and diagonal elements 0.5*Z_i^2.4, where Z_i and R_i denote the nuclear charge and the position of atom i, respectively.

    @@ -6086,7 +6332,7 @@

    Submodules
    Args:
    -
    **kwargs: keyword args to pass to get_dimensionality() method of
    +
    **kwargs: keyword args to pass to get_dimensionality() method of
    pymatgen.
    @@ -6238,7 +6484,7 @@

    Submodulesclass matminer.featurizers.structure.EwaldEnergy(accuracy=4)ΒΆ

    Bases: matminer.featurizers.base.BaseFeaturizer

    Compute the energy from Coulombic interactions.

    -

    Note: The energy is computed using _charges already defined for the structure_.

    +

    Note: The energy is computed using _charges already defined for the structure_.

    Features:
    ewald_energy - Coulomb interaction energy of the structure
    @@ -7170,7 +7416,7 @@

    Submodules*Note for nth mode, stat must be β€˜n*_mode’; e.g. stat=β€˜2nd_mode’

    +*Note for nth mode, stat must be β€˜n*_mode’; e.g. stat=β€˜2nd_mode’

    min_oxi (int): minimum site oxidation state for inclusion (e.g.,
    zero means metals/cations only)
    @@ -7220,7 +7466,7 @@

    Submodules
    -static from_preset(**kwargs)ΒΆ
    +static from_preset(preset, **kwargs)ΒΆ

    Create a SiteStatsFingerprint class according to a preset

    Args:
    @@ -7461,7 +7707,7 @@

    Submodules -
    **kwargs: any other arguments to pass into pymatgen’s XRDCalculator,
    +
    **kwargs: any other arguments to pass into pymatgen’s XRDCalculator,
    such as the type of radiation.

    @@ -7535,7 +7781,7 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.featurizers.utils.html b/docs/matminer.featurizers.utils.html index 626ffd0ad..e41e2803f 100644 --- a/docs/matminer.featurizers.utils.html +++ b/docs/matminer.featurizers.utils.html @@ -6,10 +6,10 @@ - matminer.featurizers.utils package — matminer 0.4.6 documentation + matminer.featurizers.utils package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »

    • @@ -41,6 +41,120 @@

    Navigation

    matminer.featurizers.utils packageΒΆ

    SubmodulesΒΆ

    +
    +
    +

    matminer.featurizers.utils.cgcnn moduleΒΆ

    +
    +
    +class matminer.featurizers.utils.cgcnn.AtomCustomArrayInitializer(elem_embedding)ΒΆ
    +

    Bases: object

    +

    Initialize atom feature vectors using a JSON file, which is a python +dictionary mapping from element number to a list representing the +feature vector of the element.

    +
    +
    Args:
    +
    elem_embedding_file (str): The path to the .json file
    +
    +
    +
    +__init__(elem_embedding)ΒΆ
    +

    Initialize self. See help(type(self)) for accurate signature.

    +
    + +
    + +
    +
    +class matminer.featurizers.utils.cgcnn.CIFDataWrapper(X, y, atom_init_fea, max_num_nbr=12, radius=8, dmin=0, step=0.2, random_seed=123)ΒΆ
    +

    Bases: object

    +

    Wrapper for a dataset containing pymatgen Structure objects. +This is modified from CGCNN repo’s CIFData for wrapping dataset where the +structures are stored in CIF files. +As we already have X as an iterable of pymatgen Structure objects, we can +use this wrapper instead of CIFData.

    +
    +
    +__init__(X, y, atom_init_fea, max_num_nbr=12, radius=8, dmin=0, step=0.2, random_seed=123)ΒΆ
    +
    +
    Args:
    +
    X (Series/list): An iterable of pymatgen Structure objects. +y (Series/list): target property that CGCNN is to predict. +atom_init_fea (dict): A dict of {atom type: atom feature}. +max_num_nbr (int): The max number of every atom’s neighbors. +radius (float): Cutoff radius for searching neighbors. +dmin (int): The minimum distance for constructing GaussianDistance. +step (float): The step size for constructing GaussianDistance. +random_seed (int): Random seed for shuffling the dataset.
    +
    +
    + +
    + +
    +
    +class matminer.featurizers.utils.cgcnn.CrystalGraphConvNetWrapper(orig_atom_fea_len, nbr_fea_len, atom_fea_len=64, n_conv=3, h_fea_len=128, n_h=1, classification=False)ΒΆ
    +

    Bases: object

    +

    Wrapper for CrystalGraphConvNet in the CGCNN repo and add extract_feature +function to extract the feature vector after pooling layer of CGCNN model +as features for the structures. +Please see the CrystalGraphConvNet in the CGCNN repo for more details

    +
    +
    +__init__(orig_atom_fea_len, nbr_fea_len, atom_fea_len=64, n_conv=3, h_fea_len=128, n_h=1, classification=False)ΒΆ
    +
    +
    Args:
    +

    orig_atom_fea_len (int): Number of atom features in the input. +nbr_fea_len (int): Number of bond features. +atom_fea_len (int): Number of hidden atom features

    +
    +
    in the convolutional layers.
    +

    n_conv (int): Number of convolutional layers. +h_fea_len (int): Number of hidden features after pooling. +n_h (int): Number of hidden layers after pooling. +classification (bool): Classification task or regression task.

    +
    +
    +
    + +
    +
    +extract_feature(atom_fea, nbr_fea, nbr_fea_idx, crystal_atom_idx)ΒΆ
    +

    Extract the feature vector after pooling layer of CGCNN model as +features for the structures.

    +
    +
    Args:
    +
    +
    atom_fea (Variable(torch.Tensor)): shape (N, orig_atom_fea_len)
    +
    Atom features from atom type.
    +
    nbr_fea (Variable(torch.Tensor)): shape (N, M, nbr_fea_len)
    +
    Bond features of each atom’s M neighbors.
    +
    nbr_fea_idx (torch.LongTensor): shape (N, M)
    +
    Indices of M neighbors of each atom.
    +
    crystal_atom_idx (list of torch.LongTensor): length N0
    +
    Mapping from the crystal idx to atom idx.
    +
    +
    +
    Returns:
    +
    feature (list): deep learning feature
    +
    +
    + +
    + +
    +
    +matminer.featurizers.utils.cgcnn.appropriate_kwargs(kwargs, func)ΒΆ
    +

    Auto get the appropriate kwargs according to those allowed by the func. +Args:

    +
    +
    kwargs (dict): kwargs. +func (object): function object.
    +
    +
    Returns:
    +
    filtered_dict (dict): filtered kwargs.
    +
    +
    +

    matminer.featurizers.utils.grdf moduleΒΆ

    @@ -95,7 +209,7 @@

    Submodules class matminer.featurizers.utils.grdf.Cosine(a)ΒΆ

    Bases: matminer.featurizers.utils.grdf.AbstractPairwise

    -

    Cosine pairwise function: cos(ar)

    +

    Cosine pairwise function: cos(ar)

    __init__(a)ΒΆ
    @@ -184,7 +298,7 @@

    Submodules class matminer.featurizers.utils.grdf.Sine(a)ΒΆ

    Bases: matminer.featurizers.utils.grdf.AbstractPairwise

    -

    Sine pairwise function: sin(ar)

    +

    Sine pairwise function: sin(ar)

    __init__(a)ΒΆ
    @@ -250,7 +364,7 @@

    Submodules
    -static avg_dev(weights=None)ΒΆ
    +static avg_dev(data_lst, weights=None)ΒΆ

    Mean absolute deviation of list of element data.

    This is computed by first calculating the mean of the list, and then computing the average absolute difference between each value @@ -266,7 +380,7 @@

    Submodules
    -static calc_stat(stat, weights=None)ΒΆ
    +static calc_stat(data_lst, stat, weights=None)ΒΆ

    Compute a property statistic

    Args:
    @@ -284,7 +398,7 @@

    Submodules
    -static eigenvalues(symm=False, sort=False)ΒΆ
    +static eigenvalues(data_lst, symm=False, sort=False)ΒΆ

    Return the eigenvalues of a matrix as a numpy array Args:

    @@ -296,13 +410,13 @@

    Submodules
    -static flatten()ΒΆ
    +static flatten(data_lst, weights=None)ΒΆ

    Returns a flattened copy of data_lst-as a numpy array

    -static geom_std_dev(weights=None)ΒΆ
    +static geom_std_dev(data_lst, weights=None)ΒΆ

    Geometric standard deviation

    Args:
    @@ -315,7 +429,7 @@

    Submodules
    -static holder_mean(weights=None, power=1)ΒΆ
    +static holder_mean(data_lst, weights=None, power=1)ΒΆ

    Get Holder mean Args:

    @@ -327,7 +441,7 @@

    Submodules
    -static inverse_mean(weights=None)ΒΆ
    +static inverse_mean(data_lst, weights=None)ΒΆ

    Mean of the inverse of each entry

    Args:
    @@ -340,7 +454,7 @@

    Submodules
    -static kurtosis(weights=None)ΒΆ
    +static kurtosis(data_lst, weights=None)ΒΆ

    Kurtosis of a list of data

    Args:
    @@ -353,7 +467,7 @@

    Submodules
    -static maximum(weights=None)ΒΆ
    +static maximum(data_lst, weights=None)ΒΆ

    Maximum value in a list

    Args:
    @@ -366,7 +480,7 @@

    Submodules
    -static mean(weights=None)ΒΆ
    +static mean(data_lst, weights=None)ΒΆ

    Arithmetic mean of list

    Args:
    @@ -379,7 +493,7 @@

    Submodules
    -static minimum(weights=None)ΒΆ
    +static minimum(data_lst, weights=None)ΒΆ

    Minimum value in a list

    Args:
    @@ -392,7 +506,7 @@

    Submodules
    -static mode(weights=None)ΒΆ
    +static mode(data_lst, weights=None)ΒΆ

    Mode of a list of data.

    If multiple elements occur equally-frequently (or same weight, if weights are provided), this function will return the minimum of those @@ -406,9 +520,27 @@

    Submodules +
    +static quantile(data_lst, weights=None, q=0.5)ΒΆ
    +

    Return a specific quantile. +Args:

    +
    +
    +
    data_lst (list or np.ndarray): 1D data list to be used for computing
    +
    quantiles
    +
    +

    q (float): The quantile, as a fraction between 0 and 1.

    +
    +
    +
    Returns:
    +
    (float) The computed quantile of the data_lst.
    +
    +

    +
    -static range(weights=None)ΒΆ
    +static range(data_lst, weights=None)ΒΆ

    Range of a list

    Args:
    @@ -421,7 +553,7 @@

    Submodules
    -static skewness(weights=None)ΒΆ
    +static skewness(data_lst, weights=None)ΒΆ

    Skewness of a list of data

    Args:
    @@ -434,13 +566,13 @@

    Submodules
    -static sorted()ΒΆ
    +static sorted(data_lst, weights=None)ΒΆ

    Returns the sorted data_lst

    -static std_dev(weights=None)ΒΆ
    +static std_dev(data_lst, weights=None)ΒΆ

    Standard deviation of a list of element data

    Args:
    @@ -465,10 +597,11 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.figrecipes.html b/docs/matminer.figrecipes.html index 1bc5c07b1..aa5a32962 100644 --- a/docs/matminer.figrecipes.html +++ b/docs/matminer.figrecipes.html @@ -6,10 +6,10 @@ - matminer.figrecipes package — matminer 0.4.6 documentation + matminer.figrecipes package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »

    • @@ -644,7 +644,7 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.html b/docs/matminer.html index d8fe91a20..9636f7679 100644 --- a/docs/matminer.html +++ b/docs/matminer.html @@ -6,10 +6,10 @@ - matminer package — matminer 0.4.6 documentation + matminer package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -66,6 +66,7 @@

    SubpackagesSubpackages

    @@ -56,7 +56,7 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/matminer.utils.html b/docs/matminer.utils.html index ecb1cb06f..9846eb438 100644 --- a/docs/matminer.utils.html +++ b/docs/matminer.utils.html @@ -6,10 +6,10 @@ - matminer.utils package — matminer 0.4.6 documentation + matminer.utils package — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »

    • @@ -253,11 +253,40 @@

    Submodules +
    +class matminer.utils.data.MatscholarElementDataΒΆ
    +

    Bases: matminer.utils.data.AbstractData

    +

    Class to get word embedding vectors of elements. These word embeddings were +generated using NLP + Neural Network techniques on more than 3 million +scientific abstracts.

    +

    #TODO: add citation (expected mid-2019).

    +
    +
    +__init__()ΒΆ
    +

    Initialize self. See help(type(self)) for accurate signature.

    +
    + +
    +
    +get_elemental_property(elem, property_name)ΒΆ
    +

    Get a certain elemental property for a certain element.

    +
    +
    Args:
    +
    elem - (Element) element to be assessed +property_name - (str) property to be retreived
    +
    Returns:
    +
    float, property of that element
    +
    +
    + +

    +
    class matminer.utils.data.MixingEnthalpyΒΆ

    Bases: object

    -

    Values of \Delta H^{max}_{AB} for different pairs of elements.

    +

    Values of \Delta H^{max}_{AB} for different pairs of elements.

    Based on the Miedema model. Tabulated by:
    A. Takeuchi, A. Inoue, Classification of Bulk Metallic Glasses by Atomic @@ -624,7 +653,7 @@

    Submodules
    -

    Table Of Contents

    +

    Table of Contents

    diff --git a/docs/modules.html b/docs/modules.html index 36b5a6162..8e49113e4 100644 --- a/docs/modules.html +++ b/docs/modules.html @@ -6,10 +6,10 @@ - matminer — matminer 0.4.6 documentation + matminer — matminer 0.4.8 documentation - + @@ -28,7 +28,7 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
    • @@ -144,13 +144,13 @@

    Navigation

  • modules |
    • -
  • matminer 0.4.6 documentation »
    • +
  • matminer 0.4.8 documentation »
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\ No newline at end of file diff --git a/docs_rst/changelog.rst b/docs_rst/changelog.rst index 2c7040dfc..89f29e33c 100644 --- a/docs_rst/changelog.rst +++ b/docs_rst/changelog.rst @@ -5,6 +5,14 @@ matminer Changelog ================== +**v0.4.8** + +* CGCNN featurizer / model (Q. Wang, T. Xie) +* Text-mined element embedding featurizer (A. Dunn) +* add Brgoch data set (D. Dopp) +* add quartile to PropertyStats (A. Dunn) +* minor fixes, improvements (A. Dunn, A. Jain) + **v0.4.6** * Jarvis CFID descriptors (A. Dunn, K. Choudhary) diff --git a/docs_rst/conf.py b/docs_rst/conf.py index a8d6bf629..d03fc9ecc 100644 --- a/docs_rst/conf.py +++ b/docs_rst/conf.py @@ -31,7 +31,7 @@ # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', - 'sphinx.ext.coverage', 'sphinx.ext.pngmath', + 'sphinx.ext.coverage', 'sphinx.ext.imgmath', 'sphinx.ext.ifconfig'] # Add any paths that contain templates here, relative to this directory. diff --git a/docs_rst/contributors.rst b/docs_rst/contributors.rst index dedb562eb..0c153ce08 100644 --- a/docs_rst/contributors.rst +++ b/docs_rst/contributors.rst @@ -52,4 +52,5 @@ Other * David Waroquiers (UC Louvain, Belgium) * Logan Williams (U Michigan Ann Arbor) * Aidan Thompson (Sandia National Lab) -* Kamal Choudhary (NIST) \ No newline at end of file +* Kamal Choudhary (NIST) +* Tian Xie (MIT) \ No newline at end of file diff --git a/docs_rst/dataset_summary.rst b/docs_rst/dataset_summary.rst index aaef57849..b32bb4fad 100644 --- a/docs_rst/dataset_summary.rst +++ b/docs_rst/dataset_summary.rst @@ -70,6 +70,60 @@ publisher = {Dryad Digital Repository} +------------------------- +brgoch_superhard_training +------------------------- +2574 materials used for training regressors that predict shear and bulk modulus. + +**Number of entries:** 2574 + +============= ============================================================================================================================================================ +Column Description +============= ============================================================================================================================================================ +brgoch_feats features used in brgoch study compressed to a dictionary +bulk_modulus VRH bulk modulus +composition pymatgen composition object +formula Chemical formula as a string +material_id materials project id +structure pymatgen structure object +shear_modulus VRH shear modulus +suspect_value True if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found +============= ============================================================================================================================================================ + + + +**Reference** + +Machine Learning Directed Search for Ultraincompressible, Superhard Materials +Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch +Journal of the American Chemical Society 2018 140 (31), 9844-9853 +DOI: 10.1021/jacs.8b02717 + + + +**Bibtex Formatted Citations** + +@article{doi:10.1021/jacs.8b02717, +author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah}, +title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials}, +journal = {Journal of the American Chemical Society}, +volume = {140}, +number = {31}, +pages = {9844-9853}, +year = {2018}, +doi = {10.1021/jacs.8b02717}, +note ={PMID: 30010335}, +URL = { +https://doi.org/10.1021/jacs.8b02717 +}, +eprint = { +https://doi.org/10.1021/jacs.8b02717 +} +} + + + + -------------------- castelli_perovskites -------------------- @@ -622,6 +676,48 @@ https://doi.org/10.1021/acs.jpclett.7b01046 +--------------- +glass_binary_v2 +--------------- +Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1. + +**Number of entries:** 5483 + +======= ================================================================================================================================================================================= +Column Description +======= ================================================================================================================================================================================= +formula chemical formula +gfa glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming ("AM"), 0: non-full-forming("CR") +======= ================================================================================================================================================================================= + + + +**Reference** + +https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046 + + + +**Bibtex Formatted Citations** + +@article{doi:10.1021/acs.jpclett.7b01046, +author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.}, +title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability}, +journal = {The Journal of Physical Chemistry Letters}, +volume = {8}, +number = {14}, +pages = {3434-3439}, +year = {2017}, +doi = {10.1021/acs.jpclett.7b01046}, +note ={PMID: 28697303}, +eprint = { +https://doi.org/10.1021/acs.jpclett.7b01046 + +}} + + + + ------------------ glass_ternary_hipt ------------------ diff --git a/docs_rst/featurizer_summary.rst b/docs_rst/featurizer_summary.rst index d07002855..83d6ebe49 100644 --- a/docs_rst/featurizer_summary.rst +++ b/docs_rst/featurizer_summary.rst @@ -171,6 +171,7 @@ Name Description :code:`ChemicalOrdering` How much the ordering of species in the structure differs from random `[more] `_ :code:`StructureComposition` Features related to the composition of a structure `[more] `_ :code:`XRDPowderPattern` 1D array representing powder diffraction of a structure as calculated by `[more] `_ +:code:`CGCNNFeaturizer` Features generated by training a Crystal Graph Convolutional Neural Network `[more] `_ :code:`JarvisCFID` Classical Force-Field Inspired Descriptors (CFID) from Jarvis-ML. `[more] `_ ============================================ ================================================================================================================================================================================================================================================================================================================================================================================================================ diff --git a/docs_rst/matminer.featurizers.utils.rst b/docs_rst/matminer.featurizers.utils.rst index e78806815..7209f3104 100644 --- a/docs_rst/matminer.featurizers.utils.rst +++ b/docs_rst/matminer.featurizers.utils.rst @@ -4,6 +4,14 @@ matminer.featurizers.utils package Submodules ---------- +matminer.featurizers.utils.cgcnn module +--------------------------------------- + +.. automodule:: matminer.featurizers.utils.cgcnn + :members: + :undoc-members: + :show-inheritance: + matminer.featurizers.utils.grdf module -------------------------------------- diff --git a/matminer/__init__.py b/matminer/__init__.py index 9a263f3ed..d76d40834 100644 --- a/matminer/__init__.py +++ b/matminer/__init__.py @@ -1,3 +1,3 @@ -__version__ = '0.4.6' +__version__ = '0.4.8' from matminer.figrecipes.plot import PlotlyFig diff --git a/setup.py b/setup.py index c8dec75ac..e590e81a6 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ if __name__ == "__main__": setup( name='matminer', - version='0.4.6', + version='0.4.8', description='matminer is a library that contains tools for data mining in Materials Science', long_description=open(os.path.join(module_dir, 'README.md')).read(), url='https://github.com/hackingmaterials/matminer',